From user-generated data to data-driven innovation: A research agenda to understand user privacy in digital markets

User Experience Design for Online Sports Shoe Retail Platforms: An Empirical Analysis Based on Consumer Needs

Digital technologies represented by AR (Augmented Reality), VR (Virtual Reality), and digital twins, along with the expansion of metaverse platforms and digital marketing concepts, have attracted the attention of numerous sports fashion product consumers and brands, particularly in the category of sports shoes. Therefore, in the context of digital technologies, understanding the factors that affect consumer experience and the preferences in the online purchasing process of sports shoes is very important. This study employs Latent Dirichlet Allocation topic analysis to analyze 44,110 online user posts and comments from social platforms, extracting thematic elements of consumer experience needs for purchasing sports shoes online. The information obtained is further encoded and designed into a questionnaire, which is then utilized alongside the Kano model to analyze the overall preferences of consumer experience needs. The results indicate that webpage design and basic product information are considered as Must-be attributes for user experience needs; providing information on after-sales service policies and product comment, products' special feature information, and online size testing are recognized as Performance attributes. Additionally, high-tech interaction methods, visual presentation, personalized customization, virtual try-on, apparel matching recommendations, and dressing scenario recommendations are identified as Attractive attributes. The study reveals that in the context of new digital technology development, the online shopping experience for sports shoes is enhanced across four dimensions: platform experience augmentation, product experience augmentation, user demand augmentation, and interactive experience augmentation. These four dimensions collectively constitute the holistic experience design for the online retail platform. Therefore, this research provides case references and theoretical insights for researchers and developers in the fields of brand marketing, experience design, and product service innovation.

Design of judicial public opinion supervision and intelligent decision-making model based on Bi-LSTM

Fuzzy preference modeling in intelligent decision support systems aims to improve the efficiency and accuracy of decision-making processes by incorporating fuzzy logic and preference modeling techniques. While network public opinion (NPO) has the potential to drive judicial reform and progress, it also poses challenges to the independence of the judiciary due to the negative impact of malicious public opinion. To tackle this issue within the context of intelligent decision support systems, this study provides an insightful overview of current NPO monitoring technologies. Recognizing the complexities associated with handling large-scale NPO data and mitigating significant interference, a novel judicial domain NPO monitoring model is proposed, which centers around semantic feature analysis. This model takes into account time series characteristics, binary semantic fitting, and public sentiment intensity. Notably, it leverages a bidirectional long short-term memory (Bi-LSTM) network (S-Bi-LSTM) to construct a judicial domain semantic similarity calculation model. The semantic similarity values between sentences are obtained through the utilization of a fully connected layer. Empirical evaluations demonstrate the remarkable performance of the proposed model, achieving an accuracy rate of 85.9% and an F1 value of 87.1 on the test set, surpassing existing sentence semantic similarity models. Ultimately, the proposed model significantly enhances the monitoring capabilities of judicial authorities over NPO, thereby alleviating the burden on public relations faced by judicial institutions and fostering a more equitable execution of judicial power.

Mapping Ethical Artificial Intelligence Policy Landscape: A Mixed Method Analysis

As more national governments adopt policies addressing the ethical implications of artificial intelligence, a comparative analysis of policy documents on these topics can provide valuable insights into emerging concerns and areas of shared importance. This study critically examines 57 policy documents pertaining to ethical AI originating from 24 distinct countries, employing a combination of computational text mining methods and qualitative content analysis. The primary objective is to methodically identify common themes throughout these policy documents and perform a comparative analysis of the ways in which various governments give priority to crucial matters. A total of nineteen topics were initially retrieved. Through an iterative coding process, six overarching themes were identified: principles, the protection of personal data, governmental roles and responsibilities, procedural guidelines, governance and monitoring mechanisms, and epistemological considerations. Furthermore, the research revealed 31 ethical dilemmas pertaining to AI that had been overlooked previously but are now emerging. These dilemmas have been referred to in different extents throughout the policy documents. This research makes a scholarly contribution to the expanding field of technology policy formulations at the national level by analyzing similarities and differences among countries. Furthermore, this analysis has practical ramifications for policymakers who are attempting to comprehend prevailing trends and potentially neglected domains that demand focus in the ever-evolving field of artificial intelligence.

Media Production on Smartphones: Analysis of the Timing, Content, and Context of Message Production Using Real-World Smartphone Use Data

Although media production is a critical concept in communication theory, we know surprisingly little about the timing, content, and context of individuals' production behavior. Intensive observation and analysis of 94 American adults' smartphone use over 1 week showed that although time spent in producing content was on average only about 6 percent of the amount of time spent on smartphones, the production content was more purposeful, expressive, articulate, condensed, confident, personal, and emotionally charged than consumption content. Analysis of the temporal dynamics of production suggests that the content consumed in the minute before individuals' production began to resemble the subsequently produced content. Other results suggest that content production on smartphones was fragmented, idiosyncratic, and purposeful, highlighting the impact of individuals' quick interactions with media, and the need to develop user-centric theories about how, when, and why individuals produce digital content.

Privacy concerns in social media UGC communities: Understanding user behavior sentiments in complex networks

In a digital ecosystem where large amounts of data related to user actions are generated every day, important concerns have emerged about the collection, management, and analysis of these data and, according, about user privacy. In recent years, users have been accustomed to organizing in and relying on digital communities to support and achieve their goals. In this context, the present study aims to identify the main privacy concerns in user communities on social media, and how these affect users’ online behavior. In order to better understand online communities in social networks, privacy concerns, and their connection to user behavior, we developed an innovative and original methodology that combines elements of machine learning as a technical contribution. First, a complex network visualization algorithm known as ForceAtlas2 was used through the open-source software Gephi to visually identify the nodes that form the main communities belonging to the sample of UGC collected from Twitter. Then, a sentiment analysis was applied with Textblob, an algorithm that works with machine learning on which experiments were developed with support vector classifier (SVC), multinomial naïve Bayes (MNB), logistic regression (LR), random forest, and classifier (RFC) under the theoretical frameworks of computer-aided text analysis (CATA) and natural language processing (NLP). As a result, a total of 11 user communities were identified: the positive protection software and cybersecurity and eCommerce, the negative privacy settings, personal information and social engineering, and the neutral privacy concerns, hacking, false information, impersonation and cookies data. The paper concludes with a discussion of the results and their relation to user behavior in digital environments and an outline valuable and practical insights into some techniques and challenges related to users’ personal data.

Research on the innovation of time-honored brands from the perspective of dual ethical patterns

Introduction

Innovation has become an important means to promote the high-quality development of time-honored brands. However, the research on how to stimulate innovation investment in time-honored brands, especially that conducted in the local context, is still rare. To supplement the research limitations, this study adopts the perspective of dual ethical patterns and is set in the domestic context to explore the ethical influence of traditional cultures and marketization on the innovation investment of time-honored brands.

Methods

We proposed two complementary methods, which are OLS regression and fsQCA analysis respectively, to systematically analyze the mechanism and key path of the dual ethical pattern to promote the innovation of time-honored brands.

Results

The results show that traditional culture and marketization level are both positively related to the innovation of time-honored brands. However, traditional culture and marketization level are mutually exclusive in their process of affecting the innovation of time-honored brands.

Discussion

This paper advances time-honored brands literature by highlighting the dual ethic pattern formed by traditional culture and marketization level on the innovation investment of time-honored brands. The findings respond to the academic debate between traditional culture and the innovation of time-honored brands, while enriching the research scope on the innovation mechanism of time-honored brands in the local context.

Cross-level research on the impact of self-serving leadership on employee innovation behavior: The roles of workplace anxiety and team psychological safety

Employee innovative behavior is significant in maintaining an organization's sustainable development. This study explored the impact of team psychological safety and workplace anxiety on the association between self-serving leadership and employee innovation behavior by synthesizing social information processing theory, conservation of resources theory, and ego depletion theory. We conducted a hierarchical linear model analysis using three-wave paired data collected from 86 leaders and 392 employees. The research results showed that self-serving leadership is negatively correlated with employee innovation behavior. Meanwhile, team psychological safety and workplace anxiety mediated this relationship. In addition, team psychological safety mitigates the impact of workplace anxiety on employee innovation behavior and the indirect impact of self-serving leadership on employee innovation behavior via workplace anxiety. These findings have a number of theoretical and practical implications in the domains of self-serving leadership and employee innovation behavior.

Digital communication strategies used by private companies, entrepreneurs, and public entities to attract long-stay tourists: a review

Globalization and the changes in the technological and social industries have facilitated international mobility in such a way that the distinctions between tourism and other forms of mobility closer to migration have practically become blurred. Improvements in digital communication have promoted people’s search for a better quality of life in terms of health, work, leisure, and other aspects. New forms of communication have started to gradually displace the more conventional media. In this context, private companies, entrepreneurs, and public entities have grown aware of the importance of digital communication to achieve a greater scope in their population attraction policies. In this context, the present study aims to identify which digital communication strategies are used to generate interest and attraction to the destination. In our Systematic Literature Review (SLR), we examine the relationship between the terms “lifestyle migration” and “digital communication”, obtaining 294 articles found in Web of Science (WOS) and Scopus. After the analysis, we found 16 potential articles that were linked to the research objectives. The results of the methodology are classified according to the different digital communication actions identified, namely (i) Social Network Relationship; (ii) Digital Marketing, and (iii) Virtual Reality and Augmented Reality. Therefore, this this study contributes to link lifestyle migration and digital communication to destination management offices, companies and entrepreneurs. The paper finishes with a discussion of theoretical and practical implications of research on digital communication strategies in the field of long-stay tourism.

The Influence of Technostress, Learning Goal Orientation, and Perceived Team Learning Climate on Intra-Team Knowledge Sharing and Innovative Practices Among ICT-Enabled Team Members

In ICT-enabled teams, innovation involves intensive adoption of ICTs and knowledge sharing among all members rather than a few experts. However, ICTs bring not only efficiency but also technostress, which hinders knowledge sharing and innovative practices among team members. To investigate this paradox, we drew on the job demand-control (JDC) model derived from the control theory of occupational stress to construct a theoretical framework regarding the collective influence of technostress, learning goal orientation, perceived team learning climate, and intra-team knowledge sharing on the innovative practices of ICT-enabled team members. Our multiple regression analyses of 481 ICT consultants' responses show that intra-team knowledge sharing positively influenced innovative practices; perceived team learning climate positively moderated this relationship. Further, technostress negatively influenced intra-team knowledge sharing; learning goal orientation positively influenced intra-team knowledge sharing, although the relationship demonstrated an inverted U-shape. Finally, learning goal orientation negatively moderated the relationship between technostress and intra-team knowledge sharing. Our results shed light on the paradox regarding ICT adoption, with theoretical implications for employee-driven innovation, team learning climate, intra-team knowledge sharing, learning goal orientation, and managerial practices about the design and adoption of ICT-enabled jobs.

Public perceptions of digital fashion: An analysis of sentiment and Latent Dirichlet Allocation topic modeling

Since digital technology has had a significant impact on the fashion industry, digital fashion has become a hot topic in today's society. Currently, research on digital fashion is focused on the transformation of enterprise marketing strategies and the discussion of digital technology. Despite this, the current study does not include an analysis of the audience's emotional and cognitive responses to digital fashion on social networking platforms. A comprehensive analysis and discussion of 52,891 posts about digital fashion and virtual fashion published on social networking sites was conducted using k-means clustering analysis, Latent Dirichlet Allocation (LDA) topic modeling, and sentiment analysis in this study. The study examines the public's perception and hot topics about digital fashion, as well as the industry's development situation and trends. According to the findings, both positive and neutral emotions accompany the public's attitude toward digital fashion. There is a wide range of topics covered in the discussion. Innovations in digital technology have impacted the creation of jobs, talent demand, marketing strategies, profit forms, and industrial chain innovation of fashion-related businesses. Researchers in related fields will find this study useful not only as a reference for research methods and directions, but also as a source of references for research methodology. A case study and data reference will also be provided to industry practitioners.

Using a support vector machine to determine loyalty in African, European, and North American telecoms

Brand loyalty is seen as a repeat purchase and the ability to recommend services or products. Telecommunication service providers require loyal customers to stay in business. The current study examines the impact of brand function and corporate image on customer loyalty in the telecommunication industry. The research employed a total of 971 responses from an anonymous online survey of telecommunication customers in Africa, Europe, and North America. Employing partial least squares, the study examined the relationships between brand function, corporate image, and loyalty. The result showed that brand function and corporate image have a significant positive effect on customer satisfaction. In addition, a machine learning algorithm was used to model the best prediction for consumer recommendations of products and services provided by their telecommunication service provider to friends and family.

Factors affecting short-term rental first price: A revenue management model

The aim of this paper is to conduct a revenue management study, generating a theoretical model that establishes the relationship between the factors of a Short-Term Rental apartment offered on the Airbnb marketplace or similar and its optimal rental price set when the property is first put on the market, considering not only the characteristics defined in the platform listing but also the sociodemographic characteristics of the area in which the apartment is located. The research process was structured in six phases as case study for the technology transfer model. First, research planning was conducted to estimate the time, cost, and suitability of the research topic. Second, the study design was determined to establish a technology transfer model focusing on the theory of mixed revenue management. Third, data collection about the city of Madrid was extracted from two technological databases, namely SeeTransparent based mainly on Airbnb (28 internal characteristics of the apartment) and Deskmind Research (9 sociodemographic variables of the area in which the apartment is located). Fourth, the data were prepared to create a new descriptive variable of the apartments based on geolocation. Fifth, the analysis of this study was applied to explore the correlation between the price charged per night, the 28 internal characteristics of the apartments, and the 9 sociodemographic variables of their surrounding areas. Sixth, with this integrated database, the information was transformed into multivariate inferential statistics through Exploratory Factor Analysis and Multiple Linear Regression, creating a technology transfer model (big data algorithm) that allows revenue managers to set the price of an apartment based on known information, prior to having a history of market reactions. This research process and model consider some of the factors affecting the psychological behavior of tourism consumers. Practical implications of the findings indicate that the size/capacity of the apartments used for Short-Term rentals largely determines the initial rental price set (72%). The equipment offered by the apartments has a moderate impact (18%), and the sociodemographic characteristics of the surrounding area have a minor influence (11%).

Does the digital economy promote “innovation and entrepreneurship” in rural tourism in China?

This paper focuses on the impact of the “Digital Economy” on rural entrepreneurship. Unlike previous literature, the perspective of this paper focuses on a specific industry—tourism—and identifies a new mediating mechanism by which the “Digital Economy” affects rural tourism entrepreneurship—the promotion of innovation. The paper further clarifies the fact that the “Rural Digital Economy Index,” which is a dimension of the Digital Economy Indicator System, is the key to the mechanism of action. Theoretically, first, through a literature review, this paper provides a rationale for the “Digital Economy” to promote innovation behavior by reducing the cost of innovation. Second, using a product matching model, this paper argues that a rural tourism market characterized by innovation can stimulate more entrepreneurship. Empirically, using a sample of 150 counties in the Yangtze River Delta region of China, this paper argues that the higher the digitalization index of a county’s rural economy, the more national model villages and towns this county has (all of which include product innovation in the selection criteria) and more tourism entrepreneurial activities. Econometric methods such as endogeneity, spatial econometric regression, and sensitivity analysis proved the findings robust. Our recommendation is that the Chinese government could focus on improving the innovation environment for rural residents in the future, so that entrepreneurial activities will be spontaneously stimulated by market mechanisms.

Constructing prediction models for excessive daytime sleepiness by nomogram and machine learning: A large Chinese multicenter cohort study

Objective

Although risk factors for excessive daytime sleepiness (EDS) have been reported, there are still few cohort-based predictive models for EDS in Parkinson’s disease (PD). This 1-year longitudinal study aimed to develop a predictive model of EDS in patients with PD using a nomogram and machine learning (ML).

Materials and methods

A total of 995 patients with PD without EDS were included, and clinical data during the baseline period were recorded, which included basic information as well as motor and non-motor symptoms. One year later, the presence of EDS in this population was re-evaluated. First, the baseline characteristics of patients with PD with or without EDS were analyzed. Furthermore, a Cox proportional risk regression model and XGBoost ML were used to construct a prediction model of EDS in PD.

Results

At the 1-year follow-up, EDS occurred in 260 of 995 patients with PD (26.13%). Baseline features analysis showed that EDS correlated significantly with age, age of onset (AOO), hypertension, freezing of gait (FOG). In the Cox proportional risk regression model, we included high body mass index (BMI), late AOO, low motor score on the 39-item Parkinson’s Disease Questionnaire (PDQ-39), low orientation score on the Mini-Mental State Examination (MMSE), and absence of FOG. Kaplan–Meier survival curves showed that the survival prognosis of patients with PD in the high-risk group was significantly worse than that in the low-risk group. XGBoost demonstrated that BMI, AOO, PDQ-39 motor score, MMSE orientation score, and FOG contributed to the model to different degrees, in decreasing order of importance, and the overall accuracy of the model was 71.86% after testing.

Conclusion

In this study, we showed that risk factors for EDS in patients with PD include high BMI, late AOO, a low motor score of PDQ-39, low orientation score of MMSE, and lack of FOG, and their importance decreased in turn. Our model can predict EDS in PD with relative effectivity and accuracy.

How does capability reconfiguration impact the innovation performance of Chinese manufacturing firms?

This study explores the relationship between capability reconfiguration and firm innovation performance by analyzing a sample of 375 manufacturing firms in China. The results suggest that the relationship between capability reconfiguration and innovation performance is affected by both the catch-up stage and the mode of capability reconfiguration (evolution or substitution). The catch-up stage of enterprises significantly impacts the moderating effects of innovation magnitude on the relationship between capability substitution and firm innovation performance, however, it has no obvious effects on the moderation of innovation magnitude on the relationship between capability evolution and innovation performance. This study contributes to the theory of dynamic capability and catch-up by revealing how innovation magnitude affects capability reconfiguration and subsequent innovation performance in different catch-up stages. The implication of this study is to remind managers to take full account of the innovation magnitude and catch-up stage in their decision-making.

The influence of firm’s feedbacks on user-generated content’s linguistic style matching–An explanation based on communication accommodation theory

In virtual brand communities, users and firms continuously use different or similar linguistic styles to communicate with each other. Existing literature has demonstrated that the linguistic style matching (LSM) between the coming users’ posts [user-generated content (UGC)] and existing firms’ content will influence users’ behavior, like promoting users to release more posts. However, little research has been conducted to analyze how firms’ feedbacking behaviors influence LSM. To fill the gap, this paper uses Python to measure the LSM between 69,463 posts from 9,777 users and existing firms’ generated content in the MIUI community and examines the impact of firms’ feedbacks on this LSM. The results show that the firms’ feedbacks frequency increased the LSM, but the firms’ feedbacks text length decreased the LSM. In addition, users’ textual sentiment and the published text length moderate the impact of firms’ feedbacks (e.g., frequency, text length) on LSM. Specifically, the users’ textual sentiment valence increases the positive effect of firms’ feedbacks frequency and weakens the negative effect of firms’ feedbacks text length on LSM. The users’ produced content text length reduced the positive effect of firms’ feedbacks frequency and offset the negative effect of the firms’ feedbacks text length on LSM. Further, the effects above are significant for the relatively active users but not for the inactive ones. Based on communication accommodation theory, this paper investigates the impact of firms’ feedbacks frequency and text length on subsequent users’ posting behaviors, providing an essential reference for guiding firms’ virtual brand community management.

Online Brand Community User Segments: A Text Mining Approach

There is a trend that customers increasingly join the online brand community. However, evidence shows that there are nuances between different user segments, and only a small group of users are active. Thus, one key concern marketers face is identifying and targeting specific segments and decreasing user churn rates in an online environment. To this end, this study aims to propose a UGC-based segmentation of online brand community users, identify the characteristics of each segment, and consequently reduce online brand community users' churn rate. We used python to obtain users' post data from a well-known online brand community in China between July 2012 and December 2019, resulting in 912,452 posts and 20,493 users. We then use text mining and clustering methods to segment the users and compare the differences between the segments. Three groups—information-oriented users, entertainment-oriented users, and multi-motivation users—were emerged. Our results imply that entertainment-oriented users were the most active, yet, multi-directional users have the lowest probability of churn, with a churn rate of only 0.607 times than that of users who focus either on information or entertainment. Implications for marketing and future research opportunities are discussed.

Data-driven strategies in operation management: mining user-generated content in Twitter

In recent years, the business ecosystem has focused on understanding new ways of automating, collecting, and analyzing data in order to improve products and business models. These actions allow operations management to improve prediction, value creation, optimization, and automatization. In this study, we develop a novel methodology based on data-mining techniques and apply it to identify insights regarding the characteristics of new business models in operations management. The data analyzed in the present study are user-generated content from Twitter. The results are validated using the methods based on Computer-Aided Text Analysis. Specifically, a sentimental analysis with TextBlob on which experiments are performed using vector classifier, multinomial naïve Bayes, logistic regression, and random forest classifier is used. Then, a Latent Dirichlet Allocation is applied to separate the sample into topics based on sentiments to calculate keyness and p-value. Finally, these results are analyzed with a textual analysis developed in Python. Based on the results, we identify 8 topics, of which 5 are positive (Automation, Data, Forecasting, Mobile accessibility and Employee experiences), 1 topic is negative (Intelligence Security), and 2 topics are neutral (Operational CRM, Digital teams). The paper concludes with a discussion of the main characteristics of the business models in the OM sector that use DDI. In addition, we formulate 26 research questions to be explored in future studies.

Use of Machine Learning to Mine User-Generated Content From Mobile Health Apps for Weight Loss to Assess Factors Correlated With User Satisfaction

IMPORTANCE

The effectiveness of mobile health (mHealth) apps for reducing obesity is not ideal in daily life. Therefore, it would be useful to explore factors associated with user satisfaction with weight loss apps. Currently, research on these factors from the perspective of user-generated content is lacking.

OBJECTIVE

To mine the themes and topics frequently discussed in user-generated content in mHealth apps for weight loss, explore correlations of the topics with user satisfaction and dissatisfaction, and assess whether these correlations were asymmetric.

DESIGN, SETTING, AND PARTICIPANTS

In this population-based cross-sectional study, unsupervised machine learning was used to identify themes and topics in online discussions generated between January 1, 2019, and May 20, 2021, by Chinese users of mHealth apps for weight loss.

MAIN OUTCOMES AND MEASURES

Based on the 2-factor theory, a tobit regression model was used to explore the correlation of various app discussion topics with user satisfaction and dissatisfaction. Differences of the coefficients in models of positive rating deviation (PD) and negative rating deviation (ND), defined as the difference between the users' rating of the app and the app's comprehensive rating in the app stores, were analyzed by the Wald test.

RESULTS

In total, 191 619 reviews and ratings from unique usernames were collected for 2139 weight loss apps; 86 423 reviews (45.1%) from 339 apps (15.8%) were included in the study. Most users (65 249 [75.5%]) were satisfied with the mHealth app. Eighteen topics were identified and summarized into 9 themes. Nine topics had significant positive correlations with the PD of user satisfaction, and 6 had significant negative correlations. The factor with the strongest positive correlation with the PD was celebrity effect (β = 0.307; 95% CI, 0.290-0.323), and the factor with the weakest correlation was economic cost (β = -0.426; 95% CI, -0.447 to -0.406). Nine topics had significant positive correlations with the ND of user satisfaction, whereas 7 topics had significant negative correlations. The factor with the strongest positive correlation with the ND was fitness effect (β = 1.369; 95% CI, 1.283-1.455), and the factor with the strongest negative correlation was economic cost (β = -2.813; 95% CI, -2.875 to -2.751). There were significant differences in the PD and ND of user satisfaction. Nine motivation factors (ie, value-added attributes) and 7 hygiene factors (ie, user-expected attributes) for mHealth apps were identified.

CONCLUSIONS AND RELEVANCE

In this cross-sectional study, 16 factors had asymmetric correlations with user satisfaction and dissatisfaction with weight loss apps; 7 were related to basic expected attributes of the apps and 9 to value-added attributes. By distinguishing between expected and value-added factors, the use of weight loss apps may be improved.

User Privacy, Surveillance and Public Health during COVID-19 - An Examination of Twitterverse

Online users frequently rely on social networking platforms to transmit public concerns and raise awareness about societal issues. With many government organizations actively employing social media data in recent times, the need for processing public concerns on social media has become a critical topic of interest across academic scholars and practitioners. However, the growing volume of social media data makes it difficult to process all the issues under a single umbrella, causing to overlook the main topic of interest within communication technologies, such as privacy. For example, during the COVID-19 pandemic, arguments on privacy and health issues exploded on Twitter, with several threads centered on contact tracking, health data gathering, and its usage by government agencies. To address the challenges of rising data volumes and to understand the importance of privacy concerns, particularly among users seeking greater privacy protection during this pandemic, we conduct a focused empirical analysis of user tweets about privacy. In this two-part research, our first study reveals three macro privacy issues of discussion distilled from the Twitter corpus, subsequently subdivided into 12 user privacy categories. The second study builds on the findings of the first study, focusing on the primary difficulties highlighted in the macro privacy subjects-contact tracing and digital surveillance. Using a document clustering approach, we present implications for the focal privacy topics that policymakers, agencies, and governments should consider for offering better privacy protections and help the community rebuild.

Bringing Smart Home Technology to Peer-to-Peer Accommodation: Exploring the Drivers of Intention to Stay in Smart Accommodation

COVID-19 has caused disruptions in the sharing economy for both platforms and owners, who are typically micro-businesses. Lower demand and ample supply means that users have a great deal of choice. Finding ways for properties to differentiate themselves has been a pressing need. Against this background, this paper pursued two objectives: firstly to explore the perceived functional and emotional value of smart accommodation and the factors contributing to this by adopting the Theory of Consumption Values, and secondly to examine the role of perceived value in driving intention to stay in smart accommodation in the future. 430 responses were collected to analyse the relationships among antecedents, value and intention. The results showed that the functional value of smart accommodation is associated with the perception that such accommodation represents good value for the price, smart devices are useful, they can enhance control of stay experiences, and there are resources and opportunities facilitating the use of technology. Emotional value is determined by the perception that staying in smart accommodation represents sustainable behaviour, the integration of smart home technologies offers control over the stay experience, improves the entertainment experience, aesthetics and playfulness of using technology. Emotional values are inhibited by the perception of surveillance in smart accommodation. Also, the study offers evidence of the correlation of intention with functional and emotional value. The evidence contributes to the literature by explaining the potential implications of innovative technologies for business recovery in the post-pandemic reality, exploring the applications of smart technologies in delivering tourism services, and identifying the factors in the adoption of smart homes in the hospitality sector. The findings provide practical implications for facilitating the applications of innovative technology and its adoption in home and non-home environments.

Effect of the COVID-19 pandemic on public managers' attitudes toward digital transformation

The COVID-19 pandemic has induced a process of digital acceleration and has likely changed the attitudes of local public managers toward information and communication technology (ICT). While this attitude change has been reasonably argued, it has not been systematically measured. This study narrows this gap by measuring the attitudes of public managers before and after the outbreak of the COVID-19 pandemic. Overall, this study finds that the pandemic has led public managers to be more confident in the capacity of ICT to help cities achieve their economic, social, and environmental goals and respond to challenges. Both explicit and implicit measures confirmed attitude changes. The explicit measures also indicated that the change in public managers' attitude toward ICT was similar to their change in attitude toward scientific progress and greater than their change in attitude toward other issues that have played a major role during the pandemic, namely, climate change, citizen participation, and privacy.

Evaluation of User Satisfaction and Trust of Review Platforms: Analysis of the Impact of Privacy and E-WOM in the Case of TripAdvisor

Technological advances have had many advantages like an E-WOM (Electronic Word of Mouth) that has become a very important and powerful tool for users who wish to share their knowledge, experiences and emotions about a product or service. But the use of virtual platforms may affect the privacy of users data. This present study has a twofold objective: first check if the privacy of users when using TripAdvisor, the world’s largest travel platform, has an impact on their satisfaction and trust on the platform. Secondly, the relationship between E-WOM and the variables users trust and satisfaction when using TripAdvisor is examined. In order to achieve the objectives set out, the sample of 390 persons was analyzed. The PLS-SEM method has been used to process the data and test the hypotheses. The results of the analysis have shown that there is a positive and direct relationship between TripAdvisor users’ privacy and satisfaction. The direct and positive relationship between users’ E-WOM and their degree of satisfaction and trust toward the platform has also been confirmed. This study makes a significant contribution to the academic literature on the variables studied, as previous studies presented different results.

Community Support or Funding Amount: Actual Contribution of Reward-Based Crowdfunding to Market Success of Video Game Projects on Kickstarter

The research provides empirical evidence differentiating between market success and funding success in reward-based crowdfunding campaigns of video games and hypothesizes that the actual contribution of crowdfunding is more stemming from community support and feedback rather than funding amount. The paper uses publicly available data by combining three different sources. Project data from Kickstarter, a large crowdfunding website, in the video game category are extracted and matched with market success variables of ratings and revenues from two other public sources namely Metacritic and Steamspy. Regression results indicate that once the project is successfully funded, the funding amount does not have a significant effect on market success variables. On the other hand, the number of backers as a community support variable is a significant determinant of market success in terms of higher revenues and ratings for a project. Whether the project was successfully funded or not moderates some of the relationships. Prior literature is predominantly focused on crowdfunding success in terms of financing. Yet, this study empirically demonstrates that funding does not necessarily indicate that projects will be successful in the market and further shows the actual contribution of crowdfunding to the market success of video game projects is the community engagement, not the funding amount. This study contributes to the rapidly emerging crowdfunding literature by extending its boundaries from the crowdfunding platforms themselves to the differentiated effects of crowdfunding on market success, which has not been studied thoroughly. This paper provides a new avenue of research by suggesting not solely focusing on funding outcomes but understanding, defining and explaining the dynamics of the community aspect in crowdfunding platforms with their repercussions on market success. Future work can also highlight potential differences in these effects between product groups, as well as more holistically assess market success and capture interactions within the community on crowdfunding platforms.

An Empirical Study on the Impact of E-Commerce Live Features on Consumers’ Purchase Intention: From the Perspective of Flow Experience and Social Presence

The COVID-19 pandemic and the continuous advancement of live e-commerce technology pushed the swift growth of live e-commerce in China. Based on the S–O–R theoretical framework, this study investigates the impact of live broadcast characteristics on consumers’ social presence and flow experience, along with their impact on the consumers’ consumption intention in live e-commerce scenarios through questionnaires. Using structural equation modeling, data processing and involvement were introduced as regulating variables. Host charm, interaction, and trust in the host exerted a significant positive impact on social presence. In addition, host charm and trust in host significantly affected flow experience, and social presence significantly affected flow experience. Both social presence and flow experience significantly affected consumption intention, while involvement affected all paths to some extent. Overall, this study illustrates the significance of host in live e-commerce, and consumers with low involvement should be the focus of attention in live e-commerce.

Identifying Startups Business Opportunities from UGC on Twitter Chatting: An Exploratory Analysis

The startup business ecosystem in India has experienced exponential growth. The amount of investment in Indian startups in the last decade demonstrates the strong interest of the technology industry to these business models based on innovation. In this context, the present study aims to identify investment opportunities for investors in Indian startups by identifying key indicators that characterize the startup ecosystem in India. To this end, a three steps data mining method is developed using data mining techniques. First, a sentiment analysis (SA), a machine learning approach that classifies the topics into groups expressing feelings, is applied to a dataset. Next, we develop a Latent Dirichlet Allocation (LDA) model, a topic-modeling technique that divides the sample of n = 14.531 tweets from Twitter into topics, using user-generated content (UGC) as data. Finally, in order to identify the characteristics of each topic we apply textual analysis (TA) to identify key indicators. The originality of the present study lies in the methodological process used for data analysis. Our results also contribute to the literature on startups. The results demonstrate that the Indian startup ecosystem is influenced by areas such as fintech, innovation, crowdfunding, hardware, funds, competition, artificial intelligence, augmented reality and electronic commerce. Of note, in view of the exploratory approach of the present study, the results and implications should be taken as descriptive, rather than determining for future investments in the Indian startup ecosystem.

Profile Pictures in the Digital World: Self-Photographs Predict Better Life Satisfaction

Profiles pictures as online identities represent an extension of the user’s self in the digital world. Changes in self-representation are responsible for reduced well-being in individuals in the offline world. However, whether profile picture selection predicts the well-being of internet users is unknown. To address this question, we tested the relationship between the type of profile picture (e.g., self-photographs or other pictures) used on social media and the life satisfaction of internet users, accounting for gender and personality traits that have been thought to relate to the selection of profile pictures. The results showed that individuals using self-photographs as profiles reported a higher level of life satisfaction compared to individuals using other pictures as profiles. This effect was influenced by gender, openness, and extraversion. Hierarchical regression and moderation analyses revealed that openness and profile type interacted to predict life satisfaction in women, while openness and profile picture independently predicted life satisfaction in men. Furthermore, extraversion directly predicted life satisfaction in both men and women. These results indicate that the consistency between one’s online and offline self-representation may characterize internet users’ well-being, with potential implications for digital wellness.

Towards a new era of mass data collection: Assessing pandemic surveillance technologies to preserve user privacy

Controlling the coronavirus pandemic is triggering a cross-border strategy by which national governments attempt to control the spread of the COVID-19 pandemic. A response based on sharing facts about millions of private movements and a call to study information behavior during the global health crisis has been advised worldwide. The present study aims to identify the technologies to control the COVID-19 and future pandemics with massive data collection from users' mobile devices. This research undertakes a Systematic Literature Review (SLR) of the studies about the currently available methods, strategies, and actions to collect and analyze data from users' mobile devices. In a total of 76 relevant studies, 13 technologies that are classified based on the following aspect of data and data management have been identified: (1) security; (2) destruction; (3) voluntary access; (4) time span; and (5) storage. In addition, in order to understand how these technologies can affect user privacy, 25 data points that these technologies could have access to if installed through mobile applications have been detected. The paper concludes with a discussion of important theoretical and practical implications of preserving user privacy and curbing COVID-19 infections in the global public health emergency situation.

Trends and Future Research in Electronic Marketing: A Bibliometric Analysis of Twenty Years

Electronic marketing (eM) is a flourishing phenomenon that is gaining intense concern because of a significant impact on organizational performance. Over the past few decades, the relevance of eM has been observed in numerous fields (e.g., consumers, organizational strategy, advertisement, and overall philosophy of management to understand the insights globally). To effectively maneuver the field, all stakeholders, particularly academicians and practitioners, must comprehend the current position of the eM theory and practices for dynamic utilization. A systematic bibliometric analysis can serve this issue by providing a holistic view of the publication trend and its trajectory in terms of various themes, including citations and publication metrics. This study analyzes the bibliometric data from 2000 to 2019 to reveal the most productive countries, universities, authors, journals, and prolific publications in electronic marketing. To this end, VOS viewer software was used to visualize the mapping based on co-citation, bibliographic coupling (BC), and co-occurrence (CC). The primary addition of this research is to provide an overview of eM tendencies and paths that may help researchers know the tendencies and future research directions worldwide.

A Solution for the Remote Care of Frail Elderly Individuals via Exergames

Internet of Things (IoT) solutions are a concrete answer to many needs in the healthcare framework since they enable remote support for patients and foster continuity of care. Currently, frail elderly people are among end users who most need and would benefit from IoT solutions from both a social and a healthcare point of view. Indeed, IoT technologies can provide a set of services to monitor the healthcare of the elderly or support them in order to reduce the risk of injuries, and preserve their motor and cognitive abilities. The main feature of IoT solutions for the elderly population is ease of use. Indeed, to fully exploit the potential of an IoT solution, patients should be able to autonomously deal with it. The remote-monitoring validation engineering system (ReMoVES) described here is an IoT solution that caters to the specific needs of frail elderly individuals. Its architecture was designed for use at rehabilitation centers and at patients' homes. The system is user-friendly and comfortably usable by persons who are not familiar with technology. In addition, exergames enhance patient engagement in order to curb therapy abandonment. Along with the technical presentation of the solution, a real-life scenario application is described referring to sit-to-stand activity.

Digital Citizenship and Digital Literacy in the Conditions of Social Crisis

In the present day, Internet technology and social media totally dominate as a means of communication. The media and social interaction have a two-sided nature, as the important role is not only those of the media messages to the users, but of the users to the media, too. This article aims to present the dominant importance of digital media, digital literacy transformation into a precondition for social inclusion, and an indicator of professional competence and social skills. Digital citizenship is a term that reflects the level of training and competencies, with a view to active participation in social, professional, and civic life. The article is based on two methods: Focus groups that were conducted in late 2019, which includes: Students, young mothers, pensioners, and unemployed. The second method used is the documents analysis—publications, materials, and quantitative results of research on social reactions to digital media as a source of information in the context of the COVID-19 pandemic crisis. The combination of materials and data that have been analyzed are related to the period of the lockdown between March 2020 and December 2020. In the time of global social crises and confrontations, digital media literacy has turned out to be of critical importance for the normal course of social events and their interpretations. In this regard, digital citizenship contributes to social understanding and control, as well as the individual practices in the global pandemic trajectory.

Foresight of Tourism in Kazakhstan: Experience Economy

The paper sheds a light on the interconnections between touristic sector development and regional development in Kazakhstan. The paper covers analysis of the current competitiveness of the touristic destinations in Kazakhstan. Based on qualitative and quantitative research, the study shows that there is a huge need for a transformation in marketing communications tools in order to increase the competitiveness and image of Kazakhstani tourism. The study provides potential scenarios and solutions to increase touristic attractiveness, which would lead to enticing more investors and increase tourism capacity and potential. Also, the paper provides insights in ecotourism and the regional economy by outlining the older and newer managerial and governmental approaches in supporting the entire tourism sector in Kazakhstan.

Realistic Aspects of Simulation Models for Fake News Epidemics over Social Networks

The pervasiveness of online social networks has reshaped the way people access information. Online social networks make it common for users to inform themselves online and share news among their peers, but also favor the spreading of both reliable and fake news alike. Because fake news may have a profound impact on the society at large, realistically simulating their spreading process helps evaluate the most effective countermeasures to adopt. It is customary to model the spreading of fake news via the same epidemic models used for common diseases; however, these models often miss concepts and dynamics that are peculiar to fake news spreading. In this paper, we fill this gap by enriching typical epidemic models for fake news spreading with network topologies and dynamics that are typical of realistic social networks. Specifically, we introduce agents with the role of influencers and bots in the model and consider the effects of dynamical network access patterns, time-varying engagement, and different degrees of trust in the sources of circulating information. These factors concur with making the simulations more realistic. Among other results, we show that influencers that share fake news help the spreading process reach nodes that would otherwise remain unaffected. Moreover, we emphasize that bots dramatically speed up the spreading process and that time-varying engagement and network access change the effectiveness of fake news spreading.